Digital images can contain a variety of objects such as text objects including characters, numbers, and symbols, and non-text objects. Among these objects, the text objects may provide contextual information which is particularly meaningful and useful to users. Conventional algorithms have often used scalar pixel values for processing such digital images. For example, conventional algorithms including SIFT (Scale Invariant Feature Transform) and MSER (Maximally Stable External Regions) have been used in detecting text objects in digital images based on scalar pixel values.
Most of the digital images in use today are color images. A color image typically includes color information such as a combination of RGB values, CMY values, or hue, brightness, and chroma values of each pixel in the image. In general, colors in digital color images are represented by multi-dimensional vectors (e.g., RGB values or CMY values). Accordingly, conventional algorithms that use scalar values for processing images are generally not suitable for recognizing text objects in color images. Instead, algorithms for recognizing text objects using vector values of pixels in color images, e.g., MSCR (Maximally Stable Color Region), have been used. However, such vector-based algorithms are generally much more complex and require far more computing resources than the scalar-based algorithms.
In order to reduce the complexity and computing resources, conventional schemes have used scalar-based algorithms to improve the processing speed in color images. For example, individual characters in text objects are recognized from an original color image by converting the original color image to an image having scalar pixel values. This process, however, may result in a loss of contrast between some text objects and their background, such that the characters in the text objects may not be properly recognized.